System architecture of a fully combined PET/CT scanner using LabPET™ electronics with an upgraded analog front-end optimized for PET and CT counting mode operation
Bibliographic record
Abstract
High light yield lutetium based crystals, such as LYSO, enable new potentials in medium and low energy radiation detection. Such scintillators combined with high quantum efficiency avalanche photodiodes (APD), have shown promising results for fully integrated PET/CT scanners. In such systems, a single detection apparatus is used for the acquisition of both PET and CT signals, allowing reduced hardware and potentially lower effective scanner cost. Using this approach, a prototype PET/CT scanner is developed using the LabPET™ digital electronics and a new upgraded front-end analog board optimized for PET and CT operation. The upgraded analog board has 2 × 64 acquisition channels based on LYSO crystal arrays (pixel size of 1.125 × 1.125 × 12 mm3) coupled 1:1 to pixelated APD photodetectors. The analog signal outputs are amplified by an optimized application-specific integrated circuit (ASIC) that offers dual charge sensitive pre-amplifier (CSP) and shaper gain modes for individual channels. For PET acquisition, the CSP-Shaper gain is adjustable from 8 to 48 mV/fC, while CT acquisition can operate from 65 to 390 mV/fC. Moreover, the LabPET™firmware was updated and optimized to meet the new hardware requirements. This paper describes the architecture of the prototype PET/CT scanner and focuses on the effect of these updates on the counting performance for both PET and CT acquisition modes. In PET operation, the events processing rate has shown a 15% improvement compared to the previous firmware version. Each channel has reached an average count rate of ~17 500 cps (counts per second). In CT mode, the firmware recorded a maximum average count rate of 400 000 cps per channel, thus allowing a total of ~25 million cps considering all 64 channels of one LabPET™digital board.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".